Detecting heterogeneity in and between breast cancer cell lines.

Cancer convergence Pub Date : 2020-01-01 Epub Date: 2020-02-03 DOI:10.1186/s41236-020-0010-1
Yang Shen, B U Sebastian Schmidt, Hans Kubitschke, Erik W Morawetz, Benjamin Wolf, Josef A Käs, Wolfgang Losert
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引用次数: 32

Abstract

Background: Cellular heterogeneity in tumor cells is a well-established phenomenon. Genetic and phenotypic cell-to-cell variability have been observed in numerous studies both within the same type of cancer cells and across different types of cancers. Another known fact for metastatic tumor cells is that they tend to be softer than their normal or non-metastatic counterparts. However, the heterogeneity of mechanical properties in tumor cells are not widely studied.

Results: Here we analyzed single-cell optical stretcher data with machine learning algorithms on three different breast tumor cell lines and show that similar heterogeneity can also be seen in mechanical properties of cells both within and between breast tumor cell lines. We identified two clusters within MDA-MB-231 cells, with cells in one cluster being softer than in the other. In addition, we show that MDA-MB-231 cells and MDA-MB-436 cells which are both epithelial breast cancer cell lines with a mesenchymal-like phenotype derived from metastatic cancers are mechanically more different from each other than from non-malignant epithelial MCF-10A cells.

Conclusion: Since stiffness of tumor cells can be an indicator of metastatic potential, this result suggests that metastatic abilities could vary within the same monoclonal tumor cell line.

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检测乳腺癌细胞系内部和细胞系之间的异质性。
背景:肿瘤细胞的细胞异质性是一个公认的现象。在许多研究中,在同一类型的癌细胞和不同类型的癌症中,都观察到细胞间的遗传和表型变异。转移性肿瘤细胞的另一个已知事实是,它们往往比正常或非转移性肿瘤细胞更柔软。然而,肿瘤细胞力学性质的异质性尚未得到广泛研究。结果:在这里,我们用机器学习算法分析了三种不同乳腺肿瘤细胞系的单细胞光学拉伸数据,并表明在乳腺肿瘤细胞系内部和之间的细胞力学特性中也可以看到类似的异质性。我们在MDA-MB-231细胞中发现了两个簇,其中一个簇中的细胞比另一个簇中的细胞更柔软。此外,我们发现MDA-MB-231细胞和MDA-MB-436细胞都是上皮性乳腺癌细胞系,具有源自转移性癌症的间质样表型,与非恶性上皮性MCF-10A细胞相比,它们之间的机械差异更大。结论:由于肿瘤细胞的硬度可以作为转移潜力的一个指标,这一结果表明,在同一单克隆肿瘤细胞系内,转移能力可能存在差异。
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Detecting heterogeneity in and between breast cancer cell lines. Cancer dormancy and criticality from a game theory perspective. Non-randomness of the anatomical distribution of tumors. A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer. Distinguishing mechanisms underlying EMT tristability.
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